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18th International Conference on Universal Access in Human-Computer Interaction (UAHCI) Held as Part of the 26th International Conference on Human-Computer Interaction (HCII)

作者： Sandnes, Frode Eika Oslo Metropolitan Univ N-0130 Oslo Norway

ISBN: (纸本)9783031608803;9783031608810

Information has become more accessible than ever due to web technologies, standards, and assistive technologies. Still, here are unresolved issues that can hinder equal access to all. This study reports on work-in-progress that explores how various implementation choices can affect what information, and the order information is presented to different user groups. First, potential situations are reviewed where content is rendered differently for different users. Three user groups are considered, namely default browser users, low vision users that rely on browser magnification, and screen reader users. Potential consequences of different content and presentation orders are discussed. Implications of this study include raising awareness of the importance of presentation order and the potential risk of multiple presentation orders;stakeholders should be encouraged to strive for consistent and meaningful content for all users.

关键词： accessibility low vision web presentation order html screen reader contrast semantic coding CSS sticky elements protrusion collision responsive layout reflow

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Lexical- and sentence-level processes in comprehension of written sentences

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READING AND WRITING 1997年第2期9卷 135-162页

作者： Berninger, VW Abbott, RD Alsdorf, BJ UNIV WASHINGTON 322 MILLER BOX 353600 SEATTLE WA 98195 USA

Two experiments and three measures of lexical skills (word finding, rapid automatized naming, receptive vocabulary) were administered to 24 third and 24 fifth graders. Each experiment included two between-subject variables (grade and sex) and four within-subject variables (mode of sentence presentation - one word and one-sentence-at-a-time;word type - orthographic, phonological and semantic foils;position of foil in sentence - beginning, middle, and end;and sentence type - meaningful and nonsense). The experiments contrasted on reading comprehension task: receptive sentence acceptability (Experiment 1) or expressive sentence reproduction (Experiment 2). One-word-at-a-time presentation, which highlights the lexical unit, resulted in better accuracy on the expressive task that required verbatim reproduction than on the forced choice receptive task. Word finding and rapid automatized naming that require active production of phonologically-referenced name codes, but not receptive vocabulary, were correlated with measures of word recognition and reading comprehension in the experiments and on the psychometric tests. These results are consistent with Perfetti's contention that a phonologically-based name code is the heart of lexical access.

关键词： lexical skills sentence comprehension orthographic coding phonological coding semantic coding word finding

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How disciplinary detail obscures the metacognitive potential of curriculums

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Curriculum Perspectives 2024年第3期44卷 319-335页

作者： Termaat, Annie Faculty of Arts and Education Deakin University

This paper analyses five junior secondary curriculums for their emphases on metacognition. Metacognition is an emergent skill that prepares students for their inscrutable futures, where they will need to apply their knowledge strategically to complex contexts, guided by self-knowledge. Its development requires experiential pedagogies that introduce problematic knowledge, and it is difficult to measure. English, humanities, mathematics and science curriculums for students aged between 11–16 years were coded using a qualitative, deductive approach. semantic information was ascribed to three epistemic and seven cognitive or metacognitive themes, and code totals compared for their relative emphases. All five curriculums provided pathways that could support metacognitive development, but only two, the International Baccalaureate Middle Years Programme (MYP) and the New Zealand Curriculum (NZC), explicitly recommended strategies likely to provoke the dispositions of teachers towards engaging students in complex, problematic tasks. Code totals revealed curriculum tightness. Cambridge Assessment International Education (CAIE) courses provided the greatest number of codes, but the Common Core State Standards (CC) and the Australian Curriculum (version 9) (ACv9), were similarly detailed, with up to three times as many cognitive and metacognitive references as the NZC and MYP. Although all the curriculums claimed to develop thinking skills, the three that used mandatory external assessment or benchmarking tests (CAIE, ACv9 and CC) did not offer mechanisms likely to build teacher capacity to engage their students in tasks that had intrinsic educational benefit. © The Author(s) 2024.

关键词： Cognition Curriculum Metacognition Mode semantic coding Tightness

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HIGH-FIDELITY SPEECH SYNTHESIS WITH MINIMAL SUPERVISION: ALL USING DIFFUSION MODELS 49

HIGH-FIDELITY SPEECH SYNTHESIS WITH MINIMAL SUPERVISION: ALL...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qiang, Chunyu Lie, Hao Tian, Yixin Zhao, Yi Zhang, Ying Wang, Longbiao Dang, Jianwu Tianjin Univ Sch New Media & Commun Tianjin Peoples R China Kuaishou Technol Co Ltd Beijing Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin Key Lab Cognit Comp & Applicat Tianjin Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Text-to-speech (TTS) methods have shown promising results in voice cloning, but they require a large number of labeled text-speech pairs. Minimally-supervised speech synthesis decouples TTS by combining two types of discrete speech representations(semantic & acoustic) and using two sequence-to-sequence tasks to enable training with minimal supervision. However, existing methods suffer from information redundancy and dimension explosion in semantic representation, and high-frequency waveform distortion in discrete acoustic representation. Autoregressive frameworks exhibit typical instability and uncontrollability issues. And non-autoregressive frameworks suffer from prosodic averaging caused by duration prediction models. To address these issues, we propose a minimally-supervised high-fidelity speech synthesis method, where all modules are constructed based on the diffusion models. The non-autoregressive framework enhances controllability, and the duration diffusion model enables diversified prosodic expression. Contrastive Token-Acoustic Pretraining (CTAP) is used as an intermediate semantic representation to solve the problems of information redundancy and dimension explosion in existing semantic coding methods. Mel-spectrogram is used as the acoustic representation. Both semantic and acoustic representations are predicted by continuous variable regression tasks to solve the problem of high-frequency fine-grained waveform distortion. Experimental results show that our proposed method outperforms the baseline method. We provide audio samples on our website. (1)

关键词： minimal supervision speech synthesis semantic coding diffusion model CTAP

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LANGUAGE-ORIENTED semantic LATENT REPRESENTATION FOR IMAGE TRANSMISSION 34

LANGUAGE-ORIENTED SEMANTIC LATENT REPRESENTATION FOR IMAGE T...

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34th International Workshop on Machine Learning for Signal Processing

作者： Cicchetti, Giordano Grassucci, Eleonora Park, Jihong Cho, Jinho Barbarossa, Sergio Comminiello, Danilo Sapienza Univ Rome Dept Informat Engn Elect & Telecomm Rome Italy Singapore Univ Technol & Design Singapore Singapore Deakin Univ Sch Informat Technol Burwood Vic Australia

ISBN: (纸本)9798350372267;9798350372250

In the new paradigm of semantic communication (SC), the focus is on delivering meanings behind bits by extracting semantic information from raw data. Recent advances in data-to-text models facilitate language-oriented SC, particularly for text-transformed image communication via image-to-text (I2T) encoding and text-to-image (T2I) decoding. However, although semantically aligned, the text is too coarse to precisely capture sophisticated visual features such as spatial locations, color, and texture, incurring a significant perceptual difference between intended and reconstructed images. To address this limitation, in this paper, we propose a novel language-oriented SC framework that communicates both text and a compressed image embedding and combines them using a latent diffusion model to reconstruct the intended image. Experimental results validate the potential of our approach, which transmits only 2.09% of the original image size while achieving higher perceptual similarities in noisy communication channels compared to a baseline SC method that communicates only through text. The code is available at https://***/ispamm/Img2Img-SC/.

关键词： semantic Communication semantic coding Generative Models Generative semantic Communication

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Sigmi: A user-oriented file-processing system

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Computers and Geosciences 1976年第3期1卷 187-193页

作者： Kremer, M. Lenci, M. Lesage, M.T. Ecole des Mines de Paris Centre de Recherche en Informatique Fontainebleau France

Among the many existing file-processing systems, several have been designed especially to handle geological data. This results primarily from the diversity of geological data and thus the impossibility of adapting them to a highly rigid system conceived essentially for data where the interpretive and subjective factor is practically absent. The system presented here attempts to take these factors into consideration in as much as it is possible, allowing in particular for nonnumerical data and varied processing techniques including semantic coding. In addition the system allows a free format for data input. It uses a data structure which is equally free in that it is a linear representation of a hierarchical structure which incorporates the formal language, unlike other systems where it is simply included in the data description language. © 1976.

关键词： Data-base Management System Free Structured Data Information Storage and Retrieval semantic coding

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VIDEO SEQUENCE QUANTIZER FOR VERY-LOW BIT-RATE MOVING PICTURE coding

VIDEO SEQUENCE QUANTIZER FOR VERY-LOW BIT-RATE MOVING PICTUR...

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Conference on Visual Communications and Image Processing 94

作者： YAMADA, A OHTA, M NISHITANI, T NEC CORP LTD INFORMAT TECHNOL RES LABSMIYAMAE KUKAWASAKIKANAGAWA 216JAPAN

ISBN: (纸本)081941638X

This paper describes a novel intelligent low bit rate coding technique for motion pictures, named Video Sequence Quantizer (VSQ). VSQ is one of the semantic coding. The concept is very simple. The encoder extracts motion information about target objects and transmit it as a few control parameters. The decoder has several video sequences in its database and outputs them selectively according to the transmitted parameters. It can reproduce natural movements with a simple operation compared with another semantic coding scheme called computer graphics model-based coding. We make clear the concept of VSQ and also apply it to a very low bit rate TV phone system. Computer simulation of TV phone has been done using twelve video sequences. It can naturally reproduce the speaker's movements by 80 bit/sec. VSQ is a very simple but basic concept for video coding. It will be also useful for mobile and multimedia communications.

关键词： LOW BIT RATE coding MODEL-BASED coding KNOWLEDGE-BASED coding semantic coding VIDEO coding TV-PHONE SYSTEM VECTOR QUANTIZER MOTION REPRODUCTION COMPUTER GRAPHICS VIDEO SEQUENCE

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Legal Challenges and Responses to Artificial Intelligence-Assisted Decision-Making in the International Economic Law System

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Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Zhang, Xiaojuan School of Law Henan University of Urban Construction Henan Pingdingshan467044 China

Legal judgment prediction is becoming a research hotspot in the legal field as an important artificial intelligence-assisted decision-making tool in legal case management, which is able to predict judgment results. In this paper, data from the 2018 China Law Research Cup competition is gathered, and the dataset is preprocessed in the context of international economic law. Then, a multi-task model for legal verdict prediction is proposed, and the training optimization and prediction of the model are designed using CNN, RNN, and LSTM as the semantic coding layer. The model proposed in this paper achieves a significant improvement of 8% and 6% in the accuracy of the model in the prediction of the charging task and the legal sentence task, respectively. In case outcome prediction, the accuracy of the model proposed in this paper is improved by 14.6% on average compared to the feature model-based modeling approach. © 2024 Xiaojuan Zhang, published by Sciendo.

关键词： Artificial intelligence-assisted decision making Legal verdict prediction Multi-task learning semantic coding

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An Autoencoder-Based Task-Oriented semantic Communication System for M2M Communication

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ALGORITHMS 2024年第11期17卷 492-492页

作者： Samarathunga, Prabhath Rezaei, Hossein Lokumarambage, Maheshi Sivalingam, Thushan Rajatheva, Nandana Fernando, Anil Univ Strathclyde Dept Comp & Informat Sci Glasgow City G1 1XQ Scotland Univ Oulu Ctr Wireless Commun Oulu 90570 Finland

semantic communication (SC) is a communication paradigm that has gained significant attention, as it offers a potential solution to move beyond Shannon's formulation in bandwidth-limited communication channels by delivering the semantic meaning of the message rather than its exact form. In this paper, we propose an autoencoder-based SC system for transmitting images between two machines over error-prone channels to support emerging applications such as VIoT, XR, M2M, and M2H communications. The proposed autoencoder architecture, with a semantically modeled encoder and decoder, transmits image data as a reduced-dimension vector (latent vector) through an error-prone channel. The decoder then reconstructs the image to determine its M2M implications. The autoencoder is trained for different noise levels under various channel conditions, and both image quality and classification accuracy are used to evaluate the system's efficacy. A CNN image classifier measures accuracy, as no image quality metric is available for SC yet. The simulation results show that all proposed autoencoders maintain high image quality and classification accuracy at high SNRs, while the autoencoder trained with zero noise underperforms other trained autoencoders at moderate SNRs. The results further indicate that all other proposed autoencoders trained under different noise levels are highly robust against channel impairments. We compare the proposed system against a comparable JPEG transmission system, and results reveal that the proposed system outperforms the JPEG system in compression efficiency by up to 50% and in received image quality with an image coding gain of up to 17 dB.

关键词： autoencoder error-correcting codes M2M communication polar codes semantic coding semantic communication successive cancellation decoding

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A novel method of symbolic representation in diving data mining: A case study of highways in China

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2018年第24期30卷

作者： Sun, Chuan Liu, Wei Chu, Duanfeng Li, Wushuang Lu, Zhenji Wang, Jianyu Huanggang Normal Univ Coll Transportat Huanggang Peoples R China Shenzhen Univ Coll Informat Engn Shenzhen Peoples R China Wuhan Univ Technol Intelligent Transport Syst Res Ctr Wuhan Hubei Peoples R China Shihezi Univ Coll Informat Sci & Technol Shihezi Peoples R China Delft Univ Technol Fac Mech Maritime & Mat Engn Delft Netherlands

Vehicle field test can be conducted smoothly because of the automobile-mounted monitoring system and abundant diving data have been collected. Driving data mining is in an urgent need of new thoughts introduced to break through the original technical bottleneck. This paper presented a novel method of symbolic representation in diving data mining and applied the idea of time series symbolization to traffic engineering. The sample data is processed by normalization, dimensionality reduction, discretization, and symbolization based on the three steps of symbolic aggregate approximation (SAX) with driving data characteristics taken into adequate consideration. The results showed that the high-dimensionality miscellaneous driving time series data was rationally converted into highly readable, easy to search and locate symbolic series after semantic encoding, and the main characteristics of time series data was preserved after a substantial reduction of data dimensionality. Finally, the paper demonstrated the positive effects of this method on the analysis of actual vehicle driving safety based on case study, and it explored the application of SAX to speed and acceleration data from driving data set.

关键词： diving data semantic coding spatio-temporal symbolic aggregate approximation time series

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